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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 2126–2150 of 9051 papers

TitleStatusHype
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code—0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question AnsweringCode0
Embracing Diversity: A Multi-Perspective Approach with Soft Labels—0
Jointly Understand Your Command and Intention:Reciprocal Co-Evolution between Scene-Aware 3D Human Motion Synthesis and Analysis—0
Autoencoder-Based Framework to Capture Vocabulary Quality in NLP—0
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform—0
Innovation-exnovation dynamics on trees and trusses—0
Modeling cell differentiation in neuroblastoma: insights into development, malignancy, and treatment relapse—0
ADAGE: Active Defenses Against GNN Extraction—0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
Identifiable Multi-View Causal Discovery Without Non-Gaussianity—0
Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation—0
LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces—0
DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models—0
UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition—0
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning—0
Where Are We? Evaluating LLM Performance on African Languages—0
IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic LanguagesCode0
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models—0
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment—0
Effect of Gender Fair Job Description on Generative AI Images—0
SMT(LIA) Sampling with High Diversity—0
AIRIS2 : a Smart Gateway Diversity Algorithm for Very High-Throughput Satellite Systems—0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
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